The main motivation for using ResNet as a base network to extract features for kelp classification is its superior performance over previous deep networks [1]}. Moreover, the feature extraction is fast due to the low computational complexity of ResNets and the reduced number of floating point operations (FLOPs). Also, the feature extracted from ResNet is 2048-dimensional, which is half of the traditional 4096-dimensional feature vector of previous networks such as VGG16 [2]}. These compact features result in reduced memory requirements for storing the features of large benthic marine datasets.
